Field Of Transportation Engineering Research Articles

Modeling and simulation of technological innovation diffusion of high-performance construction materials in transportation engineering

This study focuses on the diffusion process of technological innovations in high-performance construction materials (HPCMs) within the field of transportation engineering. The aim is to explore the impact of various factors on this process through modeling and simulation. The research employs a multi-agent modeling approach to construct a simulation model of the diffusion of technological innovations of HPCMs among firms and owners. It analyzes the impact of firms’ strategy choices, knowledge sharing, and government policies on the diffusion of technological innovations of HPCMs. Through simulation experiments, this study reveals the impact of the proportion of firms choosing followership or innovation strategies on the efficiency of technology diffusion within complex networks, as well as the double-edged effect of knowledge sharing. Additionally, the findings indicate that adjustments of government policies at different stages of diffusion have a significant impact on market dynamics. This study provides theoretical foundations and practical recommendations for promoting the research and application of high-performance construction materials, thereby advancing green and sustainable development in transportation engineering.

THE IMPORTANCE OF USING SOFTWARE IN THE DEVELOPMENT OF PROFESSIONAL COMPETENCIES OF FUTURE ROAD ENGINEERS

Road engineers play a crucial role in designing, constructing, and maintaining the infrastructure that connects communities and facilitates transportation. In order to excel in this field, future road engineers must possess a combination of technical skills and interpersonal abilities known as professional competencies. One key aspect that can greatly enhance the development of these competencies is the use of software tools. This article will explore the importance of utilizing software in the education and training of future road engineers to enhance their professional competencies. By leveraging software in their learning and practice, road engineers can hone their technical abilities, improve efficiency in project management, and cultivate essential soft skills for effective collaboration and communication in the workplace. Ultimately, incorporating software into the development of professional competencies is essential for preparing future road engineers to succeed in the ever-evolving field of transportation engineering.

Shared E-Scooter Practices in Birmingham, Alabama: Analyzing Usage, Patterns, and Determinants

Shared micromobility has gained significant attention in the field of transportation engineering in recent years as an environmentally friendly, convenient, and easily accessible transportation mode. Like other medium-sized cities, Birmingham, Alabama implemented a shared micromobility pilot program in 2021 that captured the attention of local travelers. This study examined shared e-scooter usage and associated travel patterns in Birmingham using 2021–2022 field data. From these data, ArcGIS maps were used to showcase trip origins and destinations. To gain a further understanding of e-scooter travel patterns in the study area, zip code and block group densities were calculated. Additionally, a negative binomial regression model was constructed to identify determinants of shared e-scooter trips. The analysis results showed that the usage of shared e-scooters was the highest during the nighttime (9109 trips between 9 p.m. to 10 p.m.), on weekends (20,077 trips on Saturday), and in the fall season (a total of 29,024 trips). Furthermore, the research findings indicated that shared e-scooters experienced their highest utilization rates in areas with a higher proportion of educated and higher-income individuals. These findings suggest that travelers’ mode choice related to the use of micromobility modes is influenced by environmental and demographic factors. Overall, this case study offers valuable contributions to the understanding of the role of shared e-scooters in Birmingham’s transportation landscape and can guide transportation authorities in other medium-sized cities in their efforts to plan for micromobility options.

Vibrations control of railway vehicles using decentralized proportional integral derivative controller with flow direction optimization algorithm

The reduction of vibration-induced discomfort in vehicles is an important goal in the field of transportation engineering. Several mathematical models with various controlling techniques, from classical to modern, have been employed to achieve better ride comfort. Still, no comprehensive solution has yet been found. Therefore, this paper proposes a 17-degree-of-freedom (minimum number of coordinates) dynamic model of a full-scale railway vehicle integrated with wheel-rail contact forces and an active suspension system. Two controllers, termed system and force tracking controllers, suppress the vehicle body's vibrations. Based on a multi-loop control structure, three optimally tuned Proportional Integral Derivative controllers evaluate the desired control forces and performs the system controller’s action. While the force-tracking controller generates the command voltage to track that forces. The parameters of controllers are tuned with a novel metaheuristic optimization algorithm known as the flow direction algorithm (FDA), and the results are compared with two other optimization techniques, i.e., particle swarm optimization and ant colony optimization. The simulated results show that the ride comfort of the vehicle is improved with FDA, as the root mean square values of the lateral, roll, and yaw accelerations are reduced by 42.01%, 33.12%, and 48.24%, respectively. Moreover, the simulated results of the proposed model are validated with the experimental results of accelerations. The simulated results show that the proposed system tuned with the metaheuristic algorithm outperforms with a significant reduction in vehicle vibrations.

Analysis of factors influencing anti-slip pile support in tunnel landslide systems for tunnels with different burial depths

Research on the tunnel landslide system poses significant challenges in the field of transportation engineering. Investigating the factors that influence the stress between tunnels at different depths and the effectiveness of anti-sliding pile support holds practical importance for real-world engineering applications. This study utilized indoor experiments and value engineering theory to examine the stress characteristics of four tunnel lining depths and anti-sliding piles under the influence of factors such as pile spacing, sliding zone angle, and pile type. The following conclusions were drawn: for shallow tunnel depths (10 m), the tunnel lining experiences relatively low stress, but the left wall of the lining structure undergoes greater stress due to the push of the landslide in the tunnel landslide system. With increasing tunnel depth, the bias stress ratio approaches 1, and the discrepancies between various factors gradually diminish. By comprehensively analyzing the factors impacting the support of tunnels at different depths in the tunnel landslide system, it was observed that when the tunnel depth is beyond the sliding zone (≤20 m), the H-shaped composite anti-sliding pile with 2D and 6D pile spacing exhibits a higher cost-effectiveness ratio. Conversely, when the tunnel depth is within the sliding zone (≥20 m), the combination of 6D pile spacing and round piles demonstrates greater cost-effectiveness. Utilizing the principles of value engineering theory, a comprehensive analysis was conducted to determine the optimal ratio that offers better performance at a lower cost. The conclusions derived from this study serve as a valuable reference for future construction decision-making.

Predicting Soil Swelling Potential Using Soil Classification Properties

Despite extensive research on unsaturated soil behavior in recent decades, the field of transportation engineering still lacks technically and economically feasible methods for identifying expansive soils along roads, particularly in regions with limited laboratory infrastructure that are distant from developed areas in Brazil. This situation is particularly evident in the state of Acre, located in the southwest of the Brazilian Amazon, where the subgrade predominantly consists of fine, plastic soils and faces challenges of limited availability of crushed stone and high precipitation levels. The volumetric variation of expansive soils in Acre has resulted in significant financial losses and has affected the lives of the local population. This paper proposes a simplified method for the preliminary identification of expansive clays, which is based on geotechnical characterization tests that can be performed at local laboratories. The proposed method utilizes the correlation between the plasticity index (PI) and silt/clay content to determine ranges that express the highest probability of occurrence of soils susceptible to shrink-swell behavior. This method was developed by correlating geotechnical parameters of 321 samples from EMBRAPA's SiSolos database with a geotechnical database of 100 local samples.

Wide-area vehicle trajectory data based on advanced tracking and trajectory splicing technologies: Potentials in transportation research

Trajectory data of road users play a crucial role in transportation engineering and traffic safety research. Previous vehicle trajectory data, including naturalistic driving data, trajectory data captured by drones and traffic cameras, have greatly promoted the research of traffic safety, traffic simulation, and other transportation engineering fields. However, existing technologies in collecting trajectory data are limited their detection range. With the development of the sensing and tracking technologies, it is possible to track the trajectory information of road users at the wide-area level, i.e., the real-time trajectory data of all road users on the entire road. Recently, we published our data sharing platform, Tongji Road Trajectory Sharing (TJRD TS, available online at https://tjrdts.com) Platform, providing our open wide-area vehicle trajectory data for researchers, academics, and road traffic professionals. Advanced vehicle tracking and trajectory splicing technologies, such as millimeter wave radar and edge computing technology, were employed to obtain such wide-area vehicle trajectory data. In this short communication article, we brainstorm ideas of using wide-area vehicle trajectory data in traffic safety research, traffic efficiency research, and connected and autonomous vehicle (CAV) application. We hope this short communication and our published data sharing platform can serve as the steppingstone for those who are interested in road transportation engineering and road safety research using such wide-area vehicle trajectory data.

Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach

The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer travel times for commuters, but also results in a significant increase in local and global emissions. The fixed cycle of traffic lights at these intersections is one of the primary reasons for this issue. To address these challenges, applying reinforcement learning to coordinating traffic light controllers has become a highly researched topic in the field of transportation engineering. This paper focuses on the traffic signal control problem, proposing a solution using a multi-agent deep Q-learning algorithm. This study introduces a novel rewarding concept in the multi-agent environment, as the reward schemes have yet to evolve in the following years with the advancement of techniques. The goal of this study is to manage traffic networks in a more efficient manner, taking into account both sustainability and classic measures. The results of this study indicate that the proposed approach can bring about significant improvements in transportation systems. For instance, the proposed approach can reduce fuel consumption by 11% and average travel time by 13%. The results of this study demonstrate the potential of reinforcement learning in improving the coordination of traffic light controllers and reducing the negative impacts of traffic congestion in urban areas. The implementation of this proposed solution could contribute to a more sustainable and efficient transportation system in the future.

ANALYSIS OF EXISTING METHODS OF EVALUATING THE TECHNICAL CONDITION OF TRANSPORT STRUCTURES UNDER SPECIAL CONDITIONS

The technical condition of transport facilities is a critical factor for ensuring the safety, efficiency and reliability of the transport infrastructure. Operating conditions can significantly affect the technical condition of buildings, especially in conditions characterized by high humidity, extreme temperatures or chemical pollution. This is especially true for structures that are exposed to natural factors such as sea water, aggressive environments or severe frosts. The article analyzes various methods of assessing the technical condition, which are used to detect defects and search for signs of damage to transport facilities. Traditional methods such as visual inspection and non-destructive evaluation are considered, as well as newer approaches including the use of remote sensing technologies, sensor monitoring and machine learning data analysis. The main purpose of the article is to identify the advantages and disadvantages of various assessment methods, as well as to determine the most effective approaches to assessing the technical condition of transport facilities in conditions that correspond to the specifics of the transport infrastructure. The obtained results can be useful for specialists in the field of transport engineering, who are engaged in the operation of transport infrastructure and road safety. Taking into account the technical condition of the facilities, it is possible to make informed decisions regarding repair work, planning regular maintenance and establishing preventive measures aimed at maintaining the safety and reliability of transport facilities.

Comparison of Pedestrian Delay Models at Signalized Midblock Crossings Under Mixed Traffic Conditions

In the field of transportation engineering, one of the most important factors for the traffic engineers and road users (pedestrian and vehicle) is the delay which governs the LOS of a facility. Past studies shows that various delay models like HCM, Webster, and Austroads work well for the homogeneous traffic conditions. However, Limited studies were done on mixed traffic conditions like in India and in other developing countries. Apart from that, all the models were developed based on the conditions of signalized intersection whereas signalized midblock crosswalks were completely overlooked by the researchers. The present study aims to compare pedestrian field delay at signalized midblock sections with the existing models. Six mid-block crossings have been selected from Hyderabad, India and data was collected using video-graphic technique. Field Pedestrian waiting delays were measured by plotting queue length against cycle time and area under the curve was calculated using Simpson's 1/3rd rule. Field crossing delay was calculated as the difference between actual crossing time and ideal crossing time. The performance of all the models were evaluated using t-test and other performance indices like MAPE, RMSE and R2. It was observed from the statistical tests that there is a significant difference between the means of field delay and delay by existing models. Also all the existing models yielded an error of more than 20% with an R2 value of less than 0.3. Calibrated results can be used in the modelling of new pedestrian delay for signalized midblock crosswalks.

Pseudo-equivalent deterministic excitation method application for experimental reproduction of a structural response to a turbulent boundary layer excitation.

In the transportation engineering field, the turbulent boundary layer over a structure is one of the most relevant sources of structural vibration and emitted noise. Wind tunnels are still one of the best options for vibroacoustic experimental analyses for this specific problem. However, it is also true that this experimental method is not always affordable, due to several limitations-settings hard to control, time and money consumption, discrepancies among laboratories-that wind tunnel facilities present. It has already developed different methodologies to address this necessity, most of them based on the use of loudspeakers or shakers. In this work, an existing numerical method, called the pseudo-equivalent deterministic excitation method (PEDEM), is further developed for the experimental purpose of reproducing the experimental structural response of a panel subjected to a turbulent boundary layer (TBL) excitation, by using an equivalent rain-on-the-roof excitation instead; different formulations are used for the application of this approximated TBL excitation. The experimental application of PEDEM, here called X-PEDEM, is validated by comparison with experimental results of two different panels analysed in two different wind tunnel facilities.

Energy transition and sanctions as challenges to state policy in the field of transport engineering

Energy transition and sanctions as challenges to state policy in the field of transport engineering

Private Car O-D Flow Estimation Based on Automated Vehicle Monitoring Data: Theoretical Issues and Empirical Evidence

Data on the daily activity of private cars form the basis of many studies in the field of transportation engineering. In the past, in order to obtain such data, a large number of collection techniques based on travel diaries and driver interviews were used. Telematics applied to vehicles and to a broad range of economic activities has opened up new opportunities for transportation engineers, allowing a significant increase in the volume and detail level of data collected. One of the options for obtaining information on the daily activity of private cars now consists of processing data from automated vehicle monitoring (AVM). Therefore, in this context, and in order to explore the opportunity offered by telematics, this paper presents a methodology for obtaining origin–destination flows through basic info extracted from AVM/floating car data (FCD). Then, the benefits of such a procedure are evaluated through its implementation in a real test case, i.e., the Veneto region in northern Italy where full-day AVM/FCD data were available with about 30,000 vehicles surveyed and more than 388,000 trips identified. Then, the goodness of the proposed methodology for O-D flow estimation is validated through assignment to the road network and comparison with traffic count data. Taking into account aspects of vehicle-sampling observations, this paper also points out issues related to sample representativeness, both in terms of daily activities and spatial coverage. A preliminary descriptive analysis of the O-D flows was carried out, and the analysis of the revealed trip patterns is presented.

Least-looping stepping-stone-based ASM approach for transportation and triangular intuitionistic fuzzy transportation problems

Transportation problem (TP) is a popular branch of Linear Programming Problem in the field of Transportation engineering. Over the years, attempts have been made in finding improved approaches to solve the TPs. Recently, in Quddoos et al. (Int J Comput Sci Eng (IJCSE) 4(7): 1271–1274, 2012), an efficient approach, namely ASM, is proposed for solving crisp TPs. However, it is found that ASM fails to provide better optimal solution in some cases. Therefore, a new and efficient ASM appoach is proposed in this paper to enhance the inherent mechanism of the existing ASM method to solve both crisp TPs and Triangular Intuitionistic Fuzzy Transportation Problems (TIFTPs). A least-looping stepping-stone method has been employed as one of the key factors to improve the solution quality, which is an improved version of the existing stepping-stone method (Roy and Hossain in, Operation research Titus Publication, 2015). Unlike stepping stone method, least-looping stepping-stone method only deals with few selected non-basic cells under some prescribed conditions and hence minimizes the computational burden. Therefore, the framework of the proposed method (namely LS-ASM) is a combination of ASM (Quddoos et al. 2012) and least-looping stepping-stone approach. To validate the performance of LS-ASM, a set of six case studies and a real-world problem (those include both crisp TPs and TIFTPs) have been solved. The statistical results obtained by LS-ASM have been well compared with the existing popular modified distribution (MODI) method and the original ASM method, as well. The statistical results confirm the superiority of the LS-ASM over other compared algorithms with a less computationl effort.

A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis

Understanding households' behaviour in residential relocation timing is of great importance in the field of transport engineering and economics. This research aims to develop a residential relocation model by considering the potential dynamic impacts of other households' decisions and variables, including economic and demographic attributes, housing features, intra-household decision-making structures, travel mode choice, and other life-course attributes. A multivariate parametric survival model with both fixed and time-varying covariates is developed. To the best of the authors' knowledge, this study is the first paper in the literature of residential relocation timing to propose the use of a Bayesian model in contrast to the widely used classic frequentist approach and have conducted a discussion on its advantages. An emerging residential relocation dataset collected for two cities in Australia and the USA (Sydney and Chicago cities) has been used, which covers residence, vehicle ownership, occupation, education, economic and demographic attributes of respondents. A comprehensive comparison between the results of two cities and a comparison between two Bayesian and frequentist approaches are made. This study confirms the impact of life-course variables, intra-household decision-making behaviours, and sociodemographic attributes on home mobility. According to the model outputs, the accelerating or decelerating impact of explanatory variables on the relocation timing has been almost the same in the two cities. The Bayesian model was confirmed to have some advantages over the frequentist model, including being straightforward to interpret, availability of making inferences on the results, and ease of handling complex models, and optimisation convergence complexities.

Evaluating the performance of highway concessions through public-private partnerships using a fuzzy multi-criteria decision-making procedure

This paper presents a procedure to assist the analysis of the performance of highway concessions through public-private partnerships with the combined use of quantitative and linguistic variables, differing from the traditional evaluation that uses only quantitative variables in its applications. The proposed procedure uses fuzzy logic to model the linguistic variables that represent the importance of the performance evaluation categories. The procedure was applied in two Brazilian highway concessions through public-private partnerships, the concessionaires AB Nascentes das Gerais (MG-050, BR-491 and BR-265 highways) and Rota dos Coqueiros (PE-024 highway), in which weights were evaluated among the performance evaluation categories from the perspective of specialists in the field of transport engineering. The two highway concessions present the same categories of performance evaluation (that is, operational, environmental, financial and social), facilitating the application of the proposed procedure. The results show that in the evaluation of the specialists there is a tendency to attribute greater importance to the operational category in relation to the other three categories and that the current concession contracts of the two case studies are not the most adequate to present the weights of categories considering the views of experts.

Consolidation of Viscoelastic Soil With Vertical Drains for Continuous Drainage Boundary Conditions Incorporating a Fractional Derivative Model

In geotechnical engineering, vertical drainage is the most economical method for accelerating the consolidation of a large area of soft ground. In this study, we analyze the viscoelasticity of the soil and the actual drainage conditions on the top surface of the soil, and then we introduce continuous drainage boundary conditions and adopt a fractional derivative model to describe the viscoelasticity of the soil. With the use of a viscoelasticity model, the governing partial differential equation for vertical drains under continuous drainage boundary conditions is obtained. With the application of the Crump numerical inversion method, the consolidation solution for vertical drains is also obtained. Further, the rationality of the proposed solution is verified by several examples. Moreover, some examples are provided to discuss the influence of interface drainage parameters on the top surface of soil and the viscoelasticity parameters of soil on the consolidation behavior of vertical drains. The proposed method can be applied in the fields of transport engineering to predict the consolidation settlement of a foundation reinforced by vertical drains.

A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning

This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control in the era of mixed autonomy when AVs drive alongside human-driven vehicles (HV). It is the first-of-its-kind survey paper to comprehensively review literature in both transportation engineering and AI for mixed traffic modeling. We will discuss state-of-the-art applications of AI-guided methods, identify opportunities and obstacles, and raise open questions. We divide the stage of AV deployment into four phases: the pure HVs, the HV-dominated, the AV-dominated, and the pure AVs. This paper is primarily focused on the latter three phases. Models used for each phase are summarized, encompassing game theory, deep (reinforcement) learning, and imitation learning. While reviewing the methodologies, we primarily focus on the following research questions: (1) What scalable driving policies are to control a large number of AVs in mixed traffic comprised of human drivers and uncontrollable AVs? (2) How do we estimate human driver behaviors? (3) How should the driving behavior of uncontrollable AVs be modeled in the environment? (4) How are the interactions between human drivers and autonomous vehicles characterized? We also provide a list of public datasets and simulation software related to AVs. Hopefully this paper will not only inspire our transportation community to rethink the conventional models that are developed in the data-shortage era, but also start conversations with other disciplines, in particular robotics and machine learning, to join forces towards creating a safe and efficient mixed traffic ecosystem.

DEVELOPMENT OF SOFTWARE FOR INTERACTIVE VISUALIZATION OF SYSTEMS AND UNITS OF TRANSPORT VEHICLES

Problem. Constant innovations in technology lead to the development of new methods and paradigms of human interaction with the digital world. An essential requirement that determines the usefulness of visual models for solving specific problems is, in addition to the automated construction of such models, the clarity of the obtained images and the convenience of manipulating their properties. Goal. The purpose of the article is to develop software for interactive visualization of systems and units of transport vehicles. Methodology. One of the promising areas of interactive visualization is the use of augmented reality. With the advent of mass mobile devices that have the necessary characteristics to run augmented reality applications, it has become possible to solve both the main shortcomings of the systems - the lack of mobility and mass distribution. Results. When developing a web application with augmented reality technology, the mathematical and algorithmic bases of augmented reality, tools for developing web applications, developed the structure and description of the web application, developed a database that stores information about models, markers and feedback and user questions. Originality. The peculiarity of this work is the focus on the mass introduction of augmented reality in the fields of learning and design. Mobile devices, tablets and computers, which should only have a camera and an Internet connection, can be used as platforms to apply the results of the study. Practical value. The developed techniques and web application can be used directly in the design of software products for use in the field of transport engineering, business processes, design, as well as in education and other fields.

A Multivariate Analysis Framework for Vehicle Detection From Loop Data Under Heterogeneous and Less Lane Disciplined Traffic

Accurate vehicle detection in real-time is a challenging problem for engineers and researchers working in the field of transportation engineering all over the world. To detect the vehicle presence in the lane-based traffic, one of the widely used traffic detectors is inductive loop detectors (ILD). For less lane disciplined and heterogeneous traffic, researchers have suggested another traffic detector known as a multiple inductive loop detector (MILD) system. In order to extract the vehicle count from MILD data, it is required to detect vehicle presence and segment the signature of different vehicles. This work is focused on the automated processing of MILD signals to get total vehicle count information in real-time under heterogeneous and less-lane disciplined traffic conditions. This study proposes a multivariate data analysis framework for the detection and segmentation of vehicle signature from the acquired data, without significant manual intervention. The major challenge in this process is the coupling of the multi-dimensional loop data, due to cross-talk across the loops. To address this, principal component analysis (PCA) is used with the additional benefit of dimensionality reduction. Though PCA is a well-known method, its application to the current problem is not trivial and calls for the tailoring of the method. Here, a new PC selection strategy suitable for data under consideration is proposed, as the traditional approach does not fit this application and tends to be low accuracy. Subsequently, the principal components are processed using a threshold-based method, which uses the mean absolute deviation measure, to detect the vehicle presence. The results show that the developed algorithm with the proposed strategy for PC selection achieved an average vehicle count accuracy of 90.38 % whereas with the traditional approach the accuracy is 24.31 %.